smfsb 1.2.0

Python code relating to the textbook, Stochastic modelling for systems biology, third edition

smfsb for python

Python library for the book, Stochastic modelling for systems biology, third edition. This library is a Python port of the R package associated with the book.

Install

Latest stable version:

pip install smfsb

You can test your installation by typing

import smfsb

at a python prompt. If it returns silently, then it is probably installed correctly.

Note that a major breaking change has been introduced in version 1.2.0, and that the documentation has been updated to reflect this change. Following recommended good practice for numpy random number generation, random number generators are now explicity threaded through the code. This has the side-benefit of making the API more similar to the JAX version of the library.

Documentation

Note that the book, and its associated github repo is the main source of documentation for this library. The code in the book is in R, but the code in this library is supposed to mirror the R code, but in Python.

For an introduction to this library, see the python-smfsb tutorial.

Further information

For further information, see the demo directory and the API documentation. Within the demos directory, see sbmlsh-demo.py for an example of how to specify a (SEIR epidemic) model using SBML-shorthand and sbml-params.py for how to modify the parameters of models parsed from SBML (or SBML-shorthand). Also see step_cle_2df.py for a 2-d reaction-diffusion simulation. For parameter inference (from time course data), see abc-cal.py for ABC inference, abc_smc.py for ABC-SMC inference and pmmh.py for particle marginal Metropolis-Hastings MCMC-based inference. There are many other demos besides these.

You can see this package on PyPI or GitHub.

Fast simulation and inference

If you like this library but find it a little slow, you should know that there is a JAX port of this package: jax-smfsb. It requires a JAX installalation, and the API is (very) slightly modified, but it has state-of-the-art performance for simulation and inference.

Copyright 2023-2026 Darren J Wilkinson